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Showing 4736 open source projects for "learning"
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$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AI Agents Masterclass
Follow along with my AI Agents Masterclass videos
AI Agents Masterclass is an educational open-source repository designed to teach developers how to build, train, and deploy intelligent AI agents using modern tooling and workflow patterns. The project includes structured lessons, code examples, and practical exercises that cover foundational concepts like prompt engineering, chaining agents, tool usage, plan execution, evaluation, and safety considerations. It breaks down how autonomous agents interact with external systems, handle... -
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Step3-VL-10B
Multimodal model achieving SOTA performance
Step3-VL-10B is an open-source multimodal foundation model developed by StepFun AI that pushes the boundaries of what compact models can achieve by combining visual and language understanding in a single architecture. Despite having only about 10 billion parameters, it delivers performance that rivals or even surpasses much larger models (10×–20× larger) on a wide range of multimodal benchmarks covering reasoning, perception, and complex tasks, positioning it as one of the most powerful... -
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Chinese-XLNet
Chinese XLNet pre-trained model
...This model is trained on large-scale Chinese text datasets to learn linguistic patterns, long-range dependencies, and semantic nuance typical of Chinese writing, making it useful for tasks like text classification, question answering, named entity recognition, and language generation. Chinese-XLNet offers an alternative to models like BERT by emphasizing autoregressive and permutation-based learning, which can lead to performance improvements on certain benchmarks and tasks. -
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JSCamp InfoJobs
Content and exercises fromJSCamp InfoJobs
JSCamp InfoJobs is the companion repository for JSCamp InfoJobs, an intensive full-stack JavaScript bootcamp in Spanish designed to take students from fundamentals to advanced topics. It is organized into numbered modules like 00-html-css, 01-javascript, 02-react, 03-router-and-zustand, 04-node, 05-typescript, 06-inteligencia-artificial, 07-sql, 08-ci-cd, and 09-docker, mirroring the curriculum of the bootcamp.The README explains that students will build a complete project step by step... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Preguntas de entrevista para React
Questions about React for job interviews
Preguntas de entrevista para React is a comprehensive Spanish-language resource containing typical React interview questions organized from beginner to expert level. The repository is not just a list of questions: it includes detailed explanations in Spanish, making it useful as both an interview prep guide and a deeper learning reference.The content is structured in sections (beginner, intermediate, expert), covering everything from “What is React?” and JSX basics to advanced topics such as hydration, server components, useTransition, useSyncExternalStore, server actions, streaming rendering APIs, and new React 19 features. It also includes JavaScript fundamentals that are essential for React, like array methods, optional chaining, arrow functions, and module syntax, so learners can strengthen their base while studying React itself. -
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Ansible for DevOps
Ansible for DevOps examples
...Rather than being theoretical, the examples span real-world infrastructure setups: multi-server orchestration, LAMP stacks, Docker deployments, Kubernetes cluster spins, rolling updates, and security hardening. You can clone the repo and play with actual scenarios using Vagrant, VirtualBox, or cloud hosts, making it ideal for both learning and reference in production readiness. The code is structured by chapter/topic, so you can pick a scenario (for example “nodejs deployment” or “ELK stack”) and dive into a fully featured Ansible solution rather than starting from scratch. Because Ansible is popular for provisioning and configuration management, this repository lowers the barrier to experimenting with real infra patterns. -
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vue2-elm
Large single page application with 45 pages built on vue2 + vuex
...It places strong emphasis on responsive UI, transitions, and user feedback patterns that feel app-like on mobile devices. Developers can study API consumption patterns and mock data handling to simulate real production constraints. As a learning resource, it gives a practical playground for debugging, performance considerations, and code readability in a real-world scale example -
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nanochat
The best ChatGPT that $100 can buy
...The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. The code is written to be read—concise training loops, transparent configs, and minimal wrappers—so you can audit each step, tweak it, and rerun without getting lost in framework indirection. -
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Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation, letting practitioners swap components without rewriting the whole stack. ... -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained inspection and modification after training. Its modular design includes tools for tree manipulation, named axes, and declarative neural network construction. ... -
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Multimodal
TorchMultimodal is a PyTorch library
This project, also known as TorchMultimodal, is a PyTorch library for building, training, and experimenting with multimodal, multi-task models at scale. The library provides modular building blocks such as encoders, fusion modules, loss functions, and transformations that support combining modalities (vision, text, audio, etc.) in unified architectures. It includes a collection of ready model classes—like ALBEF, CLIP, BLIP-2, COCA, FLAVA, MDETR, and Omnivore—that serve as reference... -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. ... -
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Elixir Code Smells
Catalog of Elixir-specific code smells
Elixir-Code-Smells is a research-driven catalog of code smells specific to the Elixir programming language. Unlike generic code smell lists, this project identifies issues emerging from Elixir’s functional, concurrent, and process-based nature. Initially compiled via grey literature (blogs, talks, forums), the catalog now includes 23 Elixir-specific smells plus 12 traditional smells adapted to Elixir. Each entry documents the name, category, problem, example, refactoring strategy, and... -
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Tencent-Hunyuan-Large
Open-source large language model family from Tencent Hunyuan
Tencent-Hunyuan-Large is the flagship open-source large language model family from Tencent Hunyuan, offering both pre-trained and instruct (fine-tuned) variants. It is designed with long-context capabilities, quantization support, and high performance on benchmarks across general reasoning, mathematics, language understanding, and Chinese / multilingual tasks. It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage... -
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Surface
A server-side rendering component library for Phoenix
Surface is a component-based UI library for Phoenix LiveView that brings a declarative, template-driven approach to building interactive interfaces. Inspired by frameworks like React, it introduces components with typed properties, slots, and macros to simplify complex UIs. Developers can create reusable, encapsulated components that integrate seamlessly with LiveView’s server-rendered real-time model. Surface emphasizes readability, making templates feel closer to HTML while retaining... -
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Ax
Build LLM powered Agents and "Agentic workflows"
...We agree with many experts like "Andrew Ng" that agentic workflows are the key to unlocking the true power of large language models and what can be achieved with in-context learning. Also, we are big fans of the Stanford DSPy paper, and this library is the result of all of this coming together to build a powerful framework for you to build with. -
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KubeEdge
Kubernetes Native Edge Computing Framework (project under CNCF)
...It consists of a cloud part and an edge part, and provides core infrastructure support for networking, application deployment, and metadata synchronization between the cloud and edge. It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image recognition, event processing, and other high-level applications to the Edge. With business logic running at the Edge, much larger volumes of data can be secured & processed locally where the data is produced. With data processed at the Edge, the responsiveness is increased dramatically and data privacy is protected. -
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SurvivalManual
Libre Survival Manual for Android with offline in mind
...It is fully functional offline, which is important in the case of a catastrophe. But it doesn't have to be used only in emergency situations, it can also be useful for outdoor trips, walks, camps, and learning about nature and yourself truly. This is not only fun, but you can also train skills (fire, build shelter, ...) that you may need in a catastrophe. Some things work best with practice in a relaxed environment, so you also have time for some experiments. The refugees also are welcome to use this application to prepare and guide you for your dangerous journey. ... -
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nodejs-integration-tests-best-practices
Beyond the basics of Node.js testing
...Its main idea is testing an entire component (e.g., Microservice) as-is, through the API, with all the layers including the database but fake anything extraneous. This brings both high confidence and great developer experience. However, doing it right, fast, exhaustive and maximizing the value demand some learning and skills. This is the mission statement of this repo. Warning: You might fall in love with testing. Detailed instructions on how to write component tests in the RIGHT way including code example and reference to the example application. A Complete showcase of a typical Node.js backend with performant tests setup (50 tests in 4 seconds! ... -
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Amazon CodeGuru Profiler Python Agent
Amazon CodeGuru Profiler Python Agent
Amazon CodeGuru Profiler collects runtime performance data from your live applications and provides recommendations that can help you fine-tune your application performance. Using machine learning algorithms, CodeGuru Profiler can help you find your most expensive lines of code and suggest ways you can improve efficiency and remove CPU bottlenecks. CodeGuru Profiler provides different visualizations of profiling data to help you identify what code is running on the CPU, see how much time is consumed, and suggest ways to reduce CPU utilization. ... -
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libfabric
AWS Libfabric
...Its custom-built operating system (OS) bypass hardware interface enhances the performance of inter-instance communications, which is critical to scaling these applications. With EFA, High Performance Computing (HPC) applications using the Message Passing Interface (MPI) and Machine Learning (ML) applications using NVIDIA Collective Communications Library (NCCL) can scale to thousands of CPUs or GPUs. As a result, you get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of the AWS cloud. -
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Crossbeam
Tools for concurrent programming in Rust
...We also have the RFCs repository for more high-level discussion, which is the place where we brainstorm ideas and propose substantial changes to Crossbeam. If you'd like to learn more about concurrency and non-blocking data structures, there's a list of learning resources in our wiki, which includes relevant blog posts, papers, videos, and other similar projects. The Crossbeam project adheres to the Rust Code of Conduct. This describes the minimum behavior expected from all contributors. -
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JENGINE
The solution that allows unity games update in runtime
JEngine is an out-of-the-box framework designed for Unity developers. It encapsulates powerful functions. Beginners can also get started quickly and easily create games that can be updated hotly. The master branch is the latest version that the developers think can be used normally and will not have too many problems. It is recommended to use and has the most powerful functions (fixes bugs in versions 0.6 and 0.5 at the same time); The 0.6.x branch is currently in use by most commercial... -
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Kubernetes Handbook
Cloud native application architecture practice handbook
Cloud native is a behavioral method and design concept. In its essence, all behaviors or methods that can improve resource utilization and application delivery efficiency on the cloud are cloud-native. The history of cloud computing is a history of cloud native. Kubernetes opened the prelude to cloud native 1.0. The emergence of service mesh Istio led to microservices in the post-Kubernetes era. The rise of serverless has enabled cloud native to advance from the infrastructure layer to the... -
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MiniMax-M2.5
State of the art LLM and coding model
MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before generating code. ...